Scope: This practical course will review some of the main statistical and computational challenges in the analyses of big omics data. The course will be organized around the following topics:
- Overview of main high-throughput technologies (e.g. microarrays, RNA-seq, ChIP-seq) and associated challenges (e.g. normalization, batch effects, data management, etc)
- Brief overview R and the Bioconductor project to manage, analyze and visualize big omics data
- Statistical analysis of microarray and RNA-seq data:
- Normalization
- Differential gene expression
- Gene set enrichment analysis
- Batch effects removal
- Alignments (RNA-seq only)
- Overview of other high-throughput technologies (e.g. ChIP-seq) and available tools in R/Bioconductor
- Data integration: Using R to integrate multiple data types and perform "systems biology" type analysis
- Drawbacks and limitations of high dimensional omics analysis (overfitting, inference)
This course will emphasize on how to the described methods, as well as the R programming language and the Bioconductor project, as tools to manipulate, visualize and analyze real world omics datasets. Fully reproducible examples with code, data and results will be provided to the participants.